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Multi-State Probabilistic Computing Using Floating-Body MOSFETs Based on the Potts Model for Solving Complex

Sunwoo Cheong1, Soo Hyung Lee1, Janguk Han1

  • 1College of Engineering, Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea.

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PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-state probabilistic computing system using MOSFETs to efficiently solve complex combinatorial optimization problems. The new system offers faster convergence and better energy efficiency compared to traditional methods.

Keywords:
annealingcombinatorial optimizationfloating body MOSFETmulti‐stateprobabilistic computing

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Area of Science:

  • Emerging computing paradigms
  • Solid-state device physics
  • Computational complexity

Background:

  • Probabilistic computing offers a promising approach for tackling complex combinatorial optimization problems (COPs).
  • Current methods often rely on the Ising model, which has limitations for intricate COPs.
  • Stochastic threshold switching floating-body metal-oxide-semiconductor field-effect transistors (FB-MOSFETs) present an opportunity for multi-state probabilistic bit (p-bit) implementation.

Purpose of the Study:

  • To propose and experimentally validate a multi-state probabilistic computing system based on the Potts model for solving challenging COPs.
  • To utilize FB-MOSFETs as multi-state p-bits for enhanced computational capabilities.
  • To demonstrate the system's efficiency, scalability, and energy advantages over existing methods.

Main Methods:

  • Development of a multi-state probabilistic computing system leveraging the Potts model.
  • Integration of stochastic threshold switching FB-MOSFETs as multi-state p-bits.
  • Implementation of drain voltage sharing and a one-hot sampling method for controlled probabilistic behavior and scalable annealing.
  • Experimental validation on benchmark COP instances like spin glass and max-4-cut problems.

Main Results:

  • The proposed system successfully samples a tunable Boltzmann distribution, crucial for solving COPs.
  • Experimental results show faster convergence compared to traditional computational methods.
  • The system demonstrates superior energy efficiency and a reduced time-to-solution for complex optimization tasks.
  • Validation on spin glass and max-4-cut problems confirms the system's effectiveness.

Conclusions:

  • Multi-state probabilistic computing using FB-MOSFETs provides an efficient and scalable solution for large-scale, complex COPs.
  • The proposed system offers significant advantages in terms of speed and energy consumption.
  • This approach highlights the potential of purely MOSFET-based probabilistic computing for future computational challenges.